Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9686
Title: A unified framework of latent feature learning in social media
Authors: Yuan, Z
Sang, J
Xu, C
Liu, Y 
Keywords: Deep learning
Feature learning
India buffet process
Social media
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on multimedia, 2014, v. 16, no. 6, 6810890, p. 1624-1635 How to cite?
Journal: IEEE transactions on multimedia 
Abstract: The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. Representation learning has been a fundamental problem in machine learning, and widely recognized as critical to the performance of end tasks. In this paper, we provide evidence that specially learned features will addresses the diverse, heterogeneous, and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a unified framework of latent feature learning on social media. To address the noisy, diverse, heterogeneous, and interconnected characteristics of social media data, the popular deep learning is employed due to its excellent abstract abilities. In particular, we instantiate the proposed framework by (1) designing a novel relational generative deep learning model to solve the social media link analysis task, and (2) developing a multimodal deep learning to lambda rank model towards the social image retrieval task. We show that the derived latent features lead to improvement in both of the social media tasks.
URI: http://hdl.handle.net/10397/9686
ISSN: 1520-9210
EISSN: 1941-0077
DOI: 10.1109/TMM.2014.2322338
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